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Large language models (LLMs) have rapidly evolved, becoming integral to applications ranging from conversational AI to complex reasoning tasks. However, as models grow in size and capability, effectively evaluating their performance has become increasingly challenging. Traditional benchmarking metrics like perplexity and BLEU scores often fail to capture the nuances of real-world interactions, making human-aligned evaluation …

Benchmarking Amazon Nova: A comprehensive analysis through MT-Bench and Arena-Hard-Auto Read More »

Learn more about new AI tools available from Google Shopping, like our launch of our try on feature and price alert updates. ​ 

We’re launching Web Guide, a Search Labs experiment that uses AI to intelligently organize the search results page, making it easier to find information and web pages. ​ 

At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this …

Customize Amazon Nova in Amazon SageMaker AI using Direct Preference Optimization Read More »

What does it take to enable billions of people to truly ask anything in Search?In the latest episode of the Google AI: Release Notes podcast, host Logan Kilpatrick sits … ​ 

In recent years, the emergence of large language models (LLMs) has accelerated AI adoption across various industries. However, to further augment LLMs’ capabilities and effectively use up-to-date information and domain-specific knowledge, integration with external data sources is essential. Retrieval Augmented Generation (RAG) has gained attention as an effective approach to address this challenge. RAG is …

Multi-tenant RAG implementation with Amazon Bedrock and Amazon OpenSearch Service for SaaS using JWT Read More »

Today’s enterprises increasingly rely on AI-driven applications to enhance decision-making, streamline workflows, and deliver improved customer experiences. Achieving these outcomes demands secure, timely, and accurate access to authoritative data—especially when such data resides across diverse repositories and applications within strict enterprise security boundaries. Interoperable technologies powered by open standards like the Model Context Protocol (MCP) …

Enhance generative AI solutions using Amazon Q index with Model Context Protocol – Part 1 Read More »

Learn more about new creation tools in Google Photos, like Photo to video, Remix and more. ​ 

Applications are now open for the first-ever Google for Startups Gemini Founders Forum, a two-day, in-person summit hosted by Google DeepMind and Google Cloud.Held Novem… ​ 

In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure …

Beyond accelerators: Lessons from building foundation models on AWS with Japan’s GENIAC program Read More »